我很高兴得到你的帮助！[我的问题][插入颜色框后，段落之间出现很多意外空格例如 ![这个答案][https://tex.stackexchange.com/a/219787/14409]或 ![另一个答案][https://tex.stackexchange.com/a/219569/14409]tcolorbox。但是，还有另一个问题，我猜这是将、enumerate、加在一起的副作用wrapfig。下面是 MWE。
\documentclass[a4paper,12pt]{article}
\usepackage[scaled]{helvet}
\renewcommand\familydefault{\sfdefault}
\usepackage[utf8]{inputenc}
\usepackage{pifont}
\usepackage{wrapfig}
\usepackage[framemethod=tikz]{mdframed}
\usepackage{xcolor,tcolorbox}
%% https://tex.stackexchange.com/a/126741
\newenvironment{WrapText1}[2][r]
{\wrapfigure[#2]{#1}{0.5\textwidth}\tcolorbox}
{\endtcolorbox\endwrapfigure}
\newenvironment{WrapText2}[2][r]
{\wrapfigure[#2]{#1}{0.5\textwidth}\mdframed[backgroundcolor=gray!20,
skipabove=0pt ,
skipbelow=0pt]}
{\endmdframed\endwrapfigure}
\title{}
\begin{document}
\maketitle
\section{Complex networks}
\subsection{Small-world networks}
\label{sec:small-world}
\index{Complex Network!Small-world network}
For many real world phenomena, the average path length $l$ of a network is much
smaller than that network size $n$, that is $l \ll n$. Such networks are said
to be characterising the small-world property [1,2]. In
mathematics, physics and sociology a small-world network (SWN) is a category of
networks in which most nodes are not neighbours of one another, but most nodes
can be reached from every other by a small number of \textit{hops} or
\textit{steps}. D. Watts and S. Strogatz introduced this terminology in 1998
[5] (also called WS model) that was originated from the famous
experiment made by Milgram in 1967 [3]. Milgram found that two
US citizens chosen randomly were connected by an average of six acquaintances.
\subsubsection*{\ding{228} Small-world networks in real life}
small-world networks can be found in many real-world applications, including
road maps, food chains, electric power grids, metabolite processing networks,
networks of brain neurons, voter networks, telephone call graphs, and social
influence networks. These systems comprise of many local links and fewer long
range \textit{``shortcuts''}, often use with a high degree of local clustering
but
relatively small diameter (see more detail below). Networks found in many
biological and man-made systems are ``small-world networks'', which are highly
clustered, but the minimum distance between any two randomly chosen nodes in
the
graph is short. Thus, studies on SWNs have been interested by many
researchers in a variety of fields such as mathematics, computer sciences,
physics, social sciences, etc.
\paragraph{\ding{51}}In a study of Indian physicians [10], they
have analysed and showed the structure of the Indian railway network (IRN).
Identifying the stations as nodes of the network and a train which stops at any
two stations as the edges between the nodes, Sen and co-authors measured the
average distance between an arbitrary pair of stations and find that it
depends logarithmically on the total number of stations in the country. While
from the network point of view this implies the small-world nature of the
railway
network, in practice a traveller has to change only a few trains to reach an
arbitrary destination. This implies that over the years, the railway network
has
evolved with the sole aim of becoming fast and economical; eventually its
structure has become a SWN.
\begin{WrapText2}{20}
In Goyal's study [6], the principal conditions that a network
$G$ exhibits \textit{small-world} properties are as the following:
\begin{enumerate}\itemsep1pt \parskip0pt \parsep0pt
\item The number of nodes is very large as compared to the average number
of links (the average degree), i.e. $n \gg k$
\item The network is integrated; a giant component exists and covers a large
share of the population.
\item The average distance between nodes $l$ (called characteristic path
length) in the giant component is small, i.e. $l$ is of order $ln(n)$.
\item The global clustering coefficient is high, i.e. $C \gg k/n$
\end{enumerate}
\end{WrapText2}
\subsubsection*{\ding{228} Properties of small-world networks}
Based on the definition of SWN proposed by
[1] and its extensions such as [1], we have described some
commonly used properties
of small-work networks as follows:
\begin{itemize}
\item the network has SCC.
\item the local neighbourhood is preserved (as for regular lattices).
\item the diameter of the network increases logarithmically with the number of
vertices $n$ (as for random networks).
\item the clustering coefficients are much larger than those of the random
networks.
\item The average length between two points characterising
global properties of the network was found to depend
strongly on the amount of disorder in the network.
\end{itemize}
\paragraph{\ding{51}}Another investigation on Boston subway, Latora and his
collaborators [2] showed that the whole transportation system
MBTA\footnote{Boston underground transportation system} (consists of $n = 124$
stations and $k = 124$ tunnels) and bus turns out to be a small-world with a
slight increase in the cost. This paper showed that a generic closed
transportation system can exhibit the small-world behaviour, substantiating
the
idea that, in the grand picture, the diffusion of small-world networks can be
interpreted as the need to create networks that are both globally and locally
efficient.
\begin{WrapText2}{15}
\textbf{Power-law distribution}~\\
A power law is a
special kind of mathematical relationship between
two quantities. When the number or frequency of an object or event varies as a
power of some attribute of that object (e.g., its size), the number or frequency
is said to follow a power law. For instance, the number of cities having a
certain population size is found to vary as a power of the size of the
population, and hence follows a power
law \cite{Clauset2009}.
\end{WrapText2}
\paragraph{\ding{51}}The World Wide Web has a small-world topology as well
[12]. In this paper, Adamic made a comparison between SWNs
for sites, and the corresponding random graphs, the subset of \textit{.edu}
sites was considered. Because the \textit{.edu} subset is significantly
smaller,
distances between every node could be computed. $3,456$ of the $11,000$
\textit{.edu} sites formed the largest SCC. In
summary, the largest SCCs of both sites in general
and the subset of \textit{.edu} sites are SWNs with small
average minimum distances.
\paragraph{\ding{51}}In fact, rich-species food webs with a good taxonomic
resolution display the properties of small-world behaviour [1].
Montoya and Sol\'e analysed the four large food webs and compared between real
webs and randomly generated webs. Consequently, they approved that the
clustering coefficient of both types is the same average number of links per
species. One important result is that in all cases, the clustering coefficient
is clearly larger than the one of the random networks. For the characteristic
path length, the difference between the random and real case is almost very
small.
\paragraph{}On the other hand, these scale-free networks own the power-law
behaviour means that most vertices are connected
sparsely, while a few vertices are connected intensively to many
others and play an important role in functionality illustrates the difference
between
random and scale-free network.
\end{document}
答案1
当您使用任何基于wrapfig您的环境时必须提供足够的常规段落文本，或者你将要得到非常奇怪的结果。文档对此进行了非常清晰的说明：这是该软件包的一个已知且广为人知的限制。
那些事情将要导致问题的原因包括使用wrapfig靠近以下环境：
- 章节标题；
- 任何基于环境的环境，
list包括itemize、、等enumerate。quotationquote
放一个list 在被包裹的图形很好。但是包裹一个list 大约这个数字是行不通的。